v2.0.0: Modularize project into single-responsibility packages
Replaced monolithic modules/ package with a clean architecture:
- core/ config.py, utils.py
- ai/ models.py (ResilientModel), setup.py (init_models)
- story/ planner.py, writer.py, editor.py, style_persona.py, bible_tracker.py
- marketing/ cover.py, blurb.py, fonts.py, assets.py
- export/ exporter.py
- web/ app.py (Flask factory), db.py, helpers.py, tasks.py, routes/{auth,project,run,persona,admin}.py
- cli/ engine.py (run_generation), wizard.py (BookWizard)
Flask routes split into 5 Blueprints; all templates updated with blueprint-
prefixed url_for() calls. Dockerfile and docker-compose updated to use
web.app entry point and new package paths.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
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core/__init__.py
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core/__init__.py
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core/config.py
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core/config.py
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import os
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from dotenv import load_dotenv
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# __file__ is core/config.py; app root is one level up
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_HERE = os.path.dirname(os.path.abspath(__file__))
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BASE_DIR = os.path.dirname(_HERE)
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# Ensure .env is loaded from the app root
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load_dotenv(os.path.join(BASE_DIR, ".env"))
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def get_clean_env(key, default=None):
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val = os.getenv(key, default)
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return val.strip() if val else None
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API_KEY = get_clean_env("GEMINI_API_KEY")
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GCP_PROJECT = get_clean_env("GCP_PROJECT")
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GCP_LOCATION = get_clean_env("GCP_LOCATION", "us-central1")
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MODEL_LOGIC_HINT = get_clean_env("MODEL_LOGIC", "AUTO")
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MODEL_WRITER_HINT = get_clean_env("MODEL_WRITER", "AUTO")
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MODEL_ARTIST_HINT = get_clean_env("MODEL_ARTIST", "AUTO")
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MODEL_IMAGE_HINT = get_clean_env("MODEL_IMAGE", "AUTO")
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DEFAULT_BLUEPRINT = "book_def.json"
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# --- SECURITY & ADMIN ---
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FLASK_SECRET = get_clean_env("FLASK_SECRET_KEY", "dev-secret-key-change-this")
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ADMIN_USER = get_clean_env("ADMIN_USERNAME")
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ADMIN_PASSWORD = get_clean_env("ADMIN_PASSWORD")
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if FLASK_SECRET == "dev-secret-key-change-this":
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print("⚠️ WARNING: Using default FLASK_SECRET_KEY. This is insecure for production.")
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if not API_KEY: raise ValueError("❌ CRITICAL ERROR: GEMINI_API_KEY not found.")
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# --- DATA DIRECTORIES ---
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DATA_DIR = os.path.join(BASE_DIR, "data")
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PROJECTS_DIR = os.path.join(DATA_DIR, "projects")
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PERSONAS_DIR = os.path.join(DATA_DIR, "personas")
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PERSONAS_FILE = os.path.join(PERSONAS_DIR, "personas.json")
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FONTS_DIR = os.path.join(DATA_DIR, "fonts")
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# --- ENSURE DIRECTORIES EXIST ---
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for d in [DATA_DIR, PROJECTS_DIR, PERSONAS_DIR, FONTS_DIR]:
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if not os.path.exists(d): os.makedirs(d, exist_ok=True)
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# --- AUTHENTICATION ---
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GOOGLE_CREDS = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
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if GOOGLE_CREDS:
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if not os.path.isabs(GOOGLE_CREDS):
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GOOGLE_CREDS = os.path.join(BASE_DIR, GOOGLE_CREDS)
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if os.path.exists(GOOGLE_CREDS):
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os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = GOOGLE_CREDS
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else:
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print(f"⚠️ Warning: GOOGLE_APPLICATION_CREDENTIALS file not found at: {GOOGLE_CREDS}")
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# --- DEFINITIONS ---
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LENGTH_DEFINITIONS = {
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"01": {"label": "Chapter Book", "words": "5,000 - 10,000", "chapters": 10, "depth": 1},
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"1": {"label": "Flash Fiction", "words": "500 - 1,500", "chapters": 1, "depth": 1},
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"2": {"label": "Short Story", "words": "5,000 - 10,000", "chapters": 5, "depth": 1},
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"2b": {"label": "Young Adult", "words": "50,000 - 70,000", "chapters": 25, "depth": 3},
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"3": {"label": "Novella", "words": "20,000 - 40,000", "chapters": 15, "depth": 2},
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"4": {"label": "Novel", "words": "60,000 - 80,000", "chapters": 30, "depth": 3},
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"5": {"label": "Epic", "words": "100,000+", "chapters": 50, "depth": 4}
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}
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# --- SYSTEM ---
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VERSION = "1.4.0"
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core/utils.py
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core/utils.py
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import os
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import json
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import datetime
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import time
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from core import config
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import threading
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import re
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SAFETY_SETTINGS = [
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{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
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{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
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]
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# Thread-local storage for logging context
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_log_context = threading.local()
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# Cache for dynamic pricing from AI model selection
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PRICING_CACHE = {}
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def set_log_file(filepath):
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_log_context.log_file = filepath
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def set_log_callback(callback):
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_log_context.callback = callback
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def set_progress_callback(callback):
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_log_context.progress_callback = callback
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def update_progress(percent):
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if getattr(_log_context, 'progress_callback', None):
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try: _log_context.progress_callback(percent)
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except: pass
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def clean_json(text):
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text = text.replace("```json", "").replace("```", "").strip()
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start_obj = text.find('{')
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start_arr = text.find('[')
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if start_obj == -1 and start_arr == -1: return text
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if start_obj != -1 and (start_arr == -1 or start_obj < start_arr):
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return text[start_obj:text.rfind('}')+1]
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else:
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return text[start_arr:text.rfind(']')+1]
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def sanitize_filename(name):
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if not name: return "Untitled"
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safe = "".join([c for c in name if c.isalnum() or c=='_']).replace(" ", "_")
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return safe if safe else "Untitled"
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def chapter_sort_key(ch):
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num = ch.get('num', 0)
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if isinstance(num, int): return num
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if isinstance(num, str) and num.isdigit(): return int(num)
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s = str(num).lower().strip()
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if 'prologue' in s: return -1
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if 'epilogue' in s: return 9999
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return 999
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def get_sorted_book_folders(run_dir):
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if not os.path.exists(run_dir): return []
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subdirs = [d for d in os.listdir(run_dir) if os.path.isdir(os.path.join(run_dir, d)) and d.startswith("Book_")]
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def sort_key(d):
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parts = d.split('_')
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if len(parts) > 1 and parts[1].isdigit(): return int(parts[1])
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return 0
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return sorted(subdirs, key=sort_key)
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def log_banner(phase, title):
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log(phase, f"{'─' * 18} {title} {'─' * 18}")
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def log(phase, msg):
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timestamp = datetime.datetime.now().strftime('%H:%M:%S')
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line = f"[{timestamp}] {phase:<15} | {msg}"
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print(line)
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if getattr(_log_context, 'log_file', None):
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with open(_log_context.log_file, "a", encoding="utf-8") as f:
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f.write(line + "\n")
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if getattr(_log_context, 'callback', None):
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try: _log_context.callback(phase, msg)
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except: pass
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def load_json(path):
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return json.load(open(path, 'r')) if os.path.exists(path) else None
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def create_default_personas():
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if not os.path.exists(config.PERSONAS_DIR): os.makedirs(config.PERSONAS_DIR)
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if not os.path.exists(config.PERSONAS_FILE):
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try:
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with open(config.PERSONAS_FILE, 'w') as f: json.dump({}, f, indent=2)
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except: pass
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def get_length_presets():
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presets = {}
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for k, v in config.LENGTH_DEFINITIONS.items():
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presets[v['label']] = v
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return presets
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def log_image_attempt(folder, img_type, prompt, filename, status, error=None, score=None, critique=None):
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log_path = os.path.join(folder, "image_log.json")
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entry = {
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"timestamp": int(time.time()),
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"type": img_type,
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"prompt": prompt,
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"filename": filename,
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"status": status,
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"error": str(error) if error else None,
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"score": score,
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"critique": critique
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}
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data = []
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if os.path.exists(log_path):
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try:
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with open(log_path, 'r') as f: data = json.load(f)
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except:
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pass
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data.append(entry)
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with open(log_path, 'w') as f: json.dump(data, f, indent=2)
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def get_run_folder(base_name):
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if not os.path.exists(base_name): os.makedirs(base_name)
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runs = [d for d in os.listdir(base_name) if d.startswith("run_")]
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next_num = max([int(r.split("_")[1]) for r in runs if r.split("_")[1].isdigit()] + [0]) + 1
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folder = os.path.join(base_name, f"run_{next_num}")
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os.makedirs(folder)
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return folder
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def get_latest_run_folder(base_name):
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if not os.path.exists(base_name): return None
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runs = [d for d in os.listdir(base_name) if d.startswith("run_")]
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if not runs: return None
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runs.sort(key=lambda x: int(x.split('_')[1]) if x.split('_')[1].isdigit() else 0)
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return os.path.join(base_name, runs[-1])
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def update_pricing(model_name, cost_str):
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if not model_name or not cost_str or cost_str == 'N/A': return
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try:
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in_cost = 0.0
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out_cost = 0.0
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prices = re.findall(r'(?:\$|USD)\s*([0-9]+\.?[0-9]*)', cost_str, re.IGNORECASE)
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if len(prices) >= 2:
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in_cost = float(prices[0])
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out_cost = float(prices[1])
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elif len(prices) == 1:
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in_cost = float(prices[0])
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out_cost = in_cost * 3
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if in_cost > 0:
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PRICING_CACHE[model_name] = {"input": in_cost, "output": out_cost}
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except:
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pass
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def calculate_cost(model_label, input_tokens, output_tokens, image_count=0):
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cost = 0.0
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m = model_label.lower()
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if model_label in PRICING_CACHE:
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rates = PRICING_CACHE[model_label]
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cost = (input_tokens / 1_000_000 * rates['input']) + (output_tokens / 1_000_000 * rates['output'])
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elif 'imagen' in m or image_count > 0:
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cost = (image_count * 0.04)
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else:
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if 'flash' in m:
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cost = (input_tokens / 1_000_000 * 0.075) + (output_tokens / 1_000_000 * 0.30)
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elif 'pro' in m or 'logic' in m:
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cost = (input_tokens / 1_000_000 * 3.50) + (output_tokens / 1_000_000 * 10.50)
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return round(cost, 6)
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def log_usage(folder, model_label, usage_metadata=None, image_count=0):
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if not folder or not os.path.exists(folder): return
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log_path = os.path.join(folder, "usage_log.json")
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input_tokens = 0
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output_tokens = 0
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if usage_metadata:
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try:
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input_tokens = usage_metadata.prompt_token_count
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output_tokens = usage_metadata.candidates_token_count
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except: pass
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cost = calculate_cost(model_label, input_tokens, output_tokens, image_count)
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entry = {
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"timestamp": int(time.time()),
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"date": datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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"model": model_label,
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"input_tokens": input_tokens,
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"output_tokens": output_tokens,
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"images": image_count,
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"cost": round(cost, 6)
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}
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data = {"log": [], "totals": {"input_tokens": 0, "output_tokens": 0, "images": 0, "est_cost_usd": 0.0}}
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if os.path.exists(log_path):
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try:
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loaded = json.load(open(log_path, 'r'))
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if isinstance(loaded, list): data["log"] = loaded
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elif isinstance(loaded, dict): data = loaded
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except: pass
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data["log"].append(entry)
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t_in = sum(x.get('input_tokens', 0) for x in data["log"])
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t_out = sum(x.get('output_tokens', 0) for x in data["log"])
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t_img = sum(x.get('images', 0) for x in data["log"])
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total_cost = 0.0
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for x in data["log"]:
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if 'cost' in x:
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total_cost += x['cost']
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else:
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c = 0.0
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mx = x.get('model', '').lower()
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ix = x.get('input_tokens', 0)
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ox = x.get('output_tokens', 0)
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imgx = x.get('images', 0)
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if 'flash' in mx: c = (ix / 1_000_000 * 0.075) + (ox / 1_000_000 * 0.30)
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elif 'pro' in mx or 'logic' in mx: c = (ix / 1_000_000 * 3.50) + (ox / 1_000_000 * 10.50)
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elif 'imagen' in mx or imgx > 0: c = (imgx * 0.04)
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total_cost += c
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data["totals"] = {
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"input_tokens": t_in,
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"output_tokens": t_out,
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"images": t_img,
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"est_cost_usd": round(total_cost, 4)
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}
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with open(log_path, 'w') as f: json.dump(data, f, indent=2)
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